Image identification method, electronic device, and computer program product

ABSTRACT

An image identification method, an electronic device with image identification function and a computer program product executing the image identification method with a software program are provided. The image identification method comprises steps of: proceeding texture feature extraction on a color source image to obtain a plurality of texture parameters; proceeding color feature extraction on a color source image to obtain a plurality of color momentums; and weighting the plurality of texture parameters and the plurality of color momentums to obtain an image identification parameter corresponding to the color source image.

This application claims the benefit of Taiwan application Serial No.102104154, filed Feb. 4, 2013, the subject matter of which isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates in general to an image identification method, anelectronic device and a computer program product, and more particularlyto an image identification method, an electronic device and a computerprogram product, in which determination is made according to texture andcolor attributes

2. Description of the Related Art

Along with the popularity of the Internet, the search engine whichenables the user to quickly find needed information is getting more andmore important. Most search engines only provide text search functionwhich searches relevant articles in the Internet according to the wordsinputted by the user.

Nowadays, digital images occupy a considerable percentage of Internetinformation such as shopping websites, news websites, or productintroduction. There is an increasing demand for image search function inaddition to the conventional text search function. The image searchfunction searches relevant articles in the Internet according to thecontent of image.

Although some search engines provide image search function, thetechnology used for search images is still based on text search. Thatis, the user must firstly input key words. Then, relevant images aresearched according to the texts. Such approach does not really searchimages in the database or the Internet but provides metadata for eachimage. Through the metadata, descriptions or key words are provided withrespect to the content of each image. Then, when the user wishes tosearch an image, the search engine will search the images according tothe text content of the metadata.

In other words, the pre-requisite for image search using metadata isthat the interpretation process must be performed on contents of imagedatabase to obtain the metadata used as a determination basis for imagesearch.

If the user wishes to use the images on hand as a search basis to obtaincorresponding images through a search engine, the conventional imagesearch method based on the metadata cannot be used for searching images.

Therefore, it is essential to provide a search method capable ofdirectly searching similar images from the image database.

SUMMARY OF THE INVENTION

According to one embodiment of the present invention, an imageidentification method is provided. The image identification methodcomprising steps of: proceeding texture feature extraction on a colorsource image to obtain a plurality of texture parameters; proceedingcolor feature extraction on the color source image to obtain a pluralityof color momentums; and, weighting the plurality of texture parametersand the plurality of color momentums to obtain an image identificationparameter corresponding to the color source image.

According to another embodiment of the present invention, an electronicdevice with image identification function is provided. The electronicdevice comprises a storage unit, a texture determination unit, a colordetermination unit, and an identification unit. The storage unit storesa color source image. The texture determination unit is electricallyconnected to the storage unit for proceeding texture feature extractionon the color source image to obtain a plurality of texture parameters.The color determination unit is electrically connected to the storageunit for proceeding texture feature extraction on the color source imageto obtain a plurality of color momentums. The identification unit iselectrically connected to the texture determination unit and the colordetermination unit for weighting the plurality of texture parameters andthe plurality of color momentums to obtain an image identificationparameter corresponding to the color source image.

According to an alternate embodiment of the present invention, acomputer program product storing a software program is provided. Whenthe software program is executed, an electronic device with a controllerperforms an image identification method. The image identification methodcomprises steps of: proceeding texture feature extraction on a colorsource image to obtain a plurality of texture parameters; proceedingcolor feature extraction on the color source image to obtain a pluralityof color momentums; and, weighting the plurality of texture parametersand the plurality of color momentums to obtain an image identificationparameter corresponding to the color source image.

According to another alternate embodiment of the present invention, animage identification method is provided. The image identification methodcomprises steps of: proceeding texture feature extraction on a firstcolor source image and a second color source image respectively toobtain a plurality of first texture parameters and a plurality of secondtexture parameters; weighting the plurality of first texture parametersand the plurality of second texture parameters respectively to obtain afirst texture identification parameter and a second textureidentification parameter corresponding to the first color source imageand the second color source image; and, identifying degree of similaritybetween the first color source image and the second color source imageaccording to the first texture identification parameter and the secondtexture identification parameter.

According to another alternate embodiment of the present invention, animage identification method is provided. The image identification methodcomprises steps of: proceeding color feature extraction on a first colorsource image and a second color source image to obtain a plurality offirst color momentums and a plurality of second color momentums;weighting the plurality of first color momentums and the plurality ofsecond color momentums respectively to obtain a first coloridentification parameter and a second color identification parametercorresponding to the first color source image and the second colorsource image; and, identifying degree of similarity between the firstcolor source image and the second color source image according to thefirst color identification parameter and the second color identificationparameter.

The above and other aspects of the invention will become betterunderstood with regard to the following detailed description of thepreferred but non-limiting embodiment(s). The following description ismade with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an identification flowchart for a color source image accordingto an embodiment of the present invention;

FIG. 2A is a schematic diagram illustrating a color source imagerepresented by a prime color representation method;

FIG. 2B is a schematic diagram illustrating a color source imageconverted into a gray-level source image;

FIG. 3A is a schematic diagram illustrating a gray-level source imagedivided into a plurality of gray-level blocks;

FIG. 3B is a schematic diagram illustrating discrete cosinetransformation performed on a gray-level block according to anembodiment of the present invention;

FIG. 4 is a schematic diagram illustrating a block transformation matrixcorrespondingly obtained from a gray-level block after discrete cosinetransformation is performed on a gray-level source image;

FIG. 5A is a schematic diagram illustrating a plurality of blocktransformation matrixes corresponding to respective gray-level blocksare generated after discrete cosine transformation is performed onrespective gray-level blocks;

FIG. 5B is a schematic diagram illustrating accumulation of each blocktransformation matrix of FIG. 5A;

FIG. 5C is a schematic diagram illustrating an image transformationmatrix obtained by accumulating the block transformation matrixesaccording to an embodiment of the present invention;

FIGS. 6A and 6B is a determination flowchart based on texture parametersaccording to an embodiment of the present invention;

FIG. 7A is a schematic diagram illustrating a color source imagerepresented by a prime color representation method;

FIG. 7B is a schematic diagram illustrating a color source imagerepresented by a chromaticity representation method;

FIG. 8 is a schematic diagram illustrating chromaticity values convertedto normalized chromatic values for each chromaticity pixel of the colorsource image;

FIG. 9 is a schematic diagram illustrating a color source image mappedonto a chromaticity plane according to a first normalized chromaticvalue and a second normalized chromatic value;

FIGS. 10A and 10B respectively are a flowchart of obtaining colormomentums based on color features according to an embodiment of thepresent invention;

FIG. 11 is a schematic diagram illustrating image identificationparameters generated according to a plurality of texture parameters anda plurality of color momentums;

FIG. 12A is a schematic diagram illustrating a first color source imagepresumably used for comparison;

FIG. 12B is a schematic diagram illustrating determination of degree ofsimilarity between a first color source image and a second color sourceimage according to a comparison between the two color source images;

FIG. 13 is a block diagram illustrating an electronic device with imageidentification function according to an embodiment of the presentinvention; and,

FIG. 14 is a schematic diagram illustrating image identification basedon three color source images.

DETAILED DESCRIPTION OF THE INVENTION

Since the conventional image search technology is based on metadata, thesearch engine cannot effectively provide content-based image retrieval(hereinafter, CBIR) function. Thus, the present invention provides animage search method in which determination is based on texture and colorattributes of image.

FIG. 1 is an identification flowchart for a color source image accordingto an embodiment of the present invention.

In step S11 (the left-hand side branch of the flowchart), texturefeature extraction is proceeded on a color source image to obtain aplurality of texture parameters. In step S12 (the right-hand side branchof the flowchart), color feature extraction is proceeded on the colorsource image to obtain a plurality of color momentums.

In step S13, a texture identification parameter F_(texture) composed oftexture parameters and a color identification parameter F_(color)composed of color momentums are weighted to obtain an imageidentification parameter F_(pic) corresponding to the color sourceimage.

In the image identification method of the present invention as disclosedabove, the texture identification parameter F_(texture) representing thetexture features of the image is generated in step S11 and the coloridentification parameter F_(color) representing the color features ofthe image is generated in step S12. The two steps can be performedconcurrently or sequentially.

Furthermore, when the database contains a plurality of color sourceimages and the color source images are processed according to theflowchart as shown in FIG. 1, image identification parameterscorresponding to each color source image are provided.

Suppose a to-be-tested image is a color source image A. Theidentification process as shown in FIG. 1 is performed on the colorsource image A to obtain an image identification parameter F_(pic-A)corresponding to the color source image A.

Then, the image identification parameter F_(pic-A) is compared with aplurality of image identification parameters pre-stored in the database.Among the pre-stored image identification parameters, the imageidentification parameter closest to the image identification parameterF_(pic-A) is selected. Of the database, the color source imagecorresponding to the selected image identification parameter is mostsimilar to the color source image A.

The identification process of the present invention can be divided intotwo aspects of determination. In one aspect, the texture features areused as a determination basis in image identification. In anotheraspect, the color features are used as a determination basis in imageidentification. The invention combines the two aspects of determinationand generates an image identification parameter corresponding to thecolor source image through weighting calculation. The two aspects ofdetermination are disclosed below. In practical application, the twoaspects can be separately used for determining degree of similaritybetween images.

Firstly, how the present invention uses texture features of image as adetermination basis in image identification is elaborated below. A setof texture matrixes representing different texture features of an imageare calculated in image processing designed to quantify the perceivedtexture of an image. For example, hair line, cloth stripe pattern andbamboo forest in an image provide different texture features. Here, thetexture features corresponding to the source image can be divided intosmooth texture feature, vertical texture feature, horizontal texturefeature, and slashed texture feature.

FIG. 2A is a schematic diagram illustrating a color source imagerepresented by a prime color representation method. A color source imageincludes M×N color pixels. For convenience of elaboration, it is assumedthat M=N=40, but the practical application is not limited thereto. Thatis, resolution and size of an image can be freely adjusted by anyone whois skilled in the technology field of the present invention.

The M×N color pixels contained in the color source image correspond to afirst prime color value (R, which represents a red value), a secondprime color value (G, which represents a green value) and a third primecolor value (B, which represents a blue value) respectively.

In the present embodiment, gray-level transformation is respectivelyperformed on 40×40=1,600 color pixels to obtain 1,600 gray-level pixels.Gray-level transformation is performed on the prime color values (R, G,B) corresponding to each color pixel to obtain a gray levelcorresponding to the gray-level pixel.

Let a pixel p(1,1) at a first row and a first column, be taken forexample. Gray-level transformation is performed on the pixel p(1,1)originally corresponding to a prime color value RGB_p(1,1) to obtain agray level Gray_p(1,1).

The gray-level transformation equation as shown in Equation 1 convertsthe color pixels contained in the color source image to a plurality ofcorresponding gray-level pixels respectively.

The gray level of a gray-level pixel is expressed in Equation 1.

The gray level of a gray-level pixel=0.299*(R)+0.587*(G)+0.114*(B)  (Equation 1)

Equation 1 defines converting weight of first prime color (such as0.299) corresponding to the first prime color value (R), convertingweight of second prime color (such as 0.587) corresponding to the secondprime color value (G), and converting weight of third prime color (suchas 0.114) corresponding to the third prime color value (B).

Here, the gray level corresponding to gray-level pixel is a summation ofa product of the first prime color value and the converting weight offirst prime color, a product of the second prime color value and theconverting weight of second prime color, and a product of the thirdprime color value and the converting weight of third prime color.

To transform the M×N color pixels into M×N gray-level pixels isequivalent to converting the color source image as shown in FIG. 2A intothe gray-level source image as shown in FIG. 2B.

FIG. 2B is a schematic diagram illustrating a color source imageconverted into a gray-level source image.

There is a one-to-one correspondence relationship between the gray-levelpixels of FIG. 2B and the color pixels of FIG. 2A. That is, the colorpixel RGB_p(1,1) at the first row and the first column of FIG. 2A isconverted to generate the gray level of the gray-level pixel Gray_p(1,1)at the first row and the first column of FIG. 2B.

The gray-level image is divided into a plurality of gray-level blocks.

FIG. 3A is a schematic diagram illustrating a gray-level source imagedivided into a plurality of gray-level blocks. The number of pixelscontained in each of the at least one gray-level block is determinedaccording to format of discrete cosine transformation. For instance, leta gray-level block with 8×8 pixels be the basic unit of discrete cosinetransformation. The image area is divided into a plurality of gray-levelblocks each being composed of 8×8 pixels. Given that M=N=40, the imageas shown in FIG. 3A can be divided into 5×5 gray-level blocks.

That is, both the number of gray-level blocks and the number of pixelscontained in each of the gray-level block are determined according tothe size of the color source image and format (basic unit) of discretecosine transformation.

FIG. 3B is a schematic diagram illustrating discrete cosinetransformation performed on a gray-level block according to anembodiment of the present invention. The present embodiment isequivalent to performing discrete cosine transformation (hereinafter,DCT) on 5×5=25 gray-level blocks respectively for converting thegray-level image into a frequency domain.

During discrete cosine transformation, each gray-level block correspondsto a block transformation matrix. For instance, the gray-level block atthe first row and the first column is converted into the blocktransformation matrix at the first row and the first column.

FIG. 4 is a schematic diagram illustrating a block transformation matrixcorrespondingly obtained from a gray-level block after discrete cosinetransformation is performed on a gray-level source image. FIG. 4 showsthat 5×5 block transformation matrixes are correspondingly obtainedafter discrete cosine transformation is performed on the 5×5 gray-levelblocks contained in the gray-level source image.

As indicated in FIG. 4, the converting values contained in each blocktransformation matrix can be divided into different areas according tocorresponding positions of the converting values. Let the enlarged blocktransformation matrix dct_B(1,1) at the first row and the first columnbe taken for example. The block transformation matrix dct_B(1,1) can bedivided into a smooth texture area, a vertical texture area, ahorizontal texture area, a slashed texture area, and a high-frequencyarea.

After discrete cosine transformation is performed on an image, theconverting values at different positions of the frequency domain of theimage can be used to indicate intensity and direction of the texture ofthe source image.

When the smooth texture area (DC) at the top-left corner of the blocktransformation matrix has a large converting value, this implies thatthe color source image is mostly composed of smooth area.

When the vertical texture area at the top-right corner of the blocktransformation matrix has a large converting value, this implies thatmost areas of the color source image have vertical distribution. Forinstance, the content of the color source image is a bamboo forest.

When the horizontal texture area at the bottom-left corner of the blocktransformation matrix has a large converting value, this implies thatmost areas of the color source image have horizontal distribution. Forinstance, the color source image illustrates people in prison jumpsuits.

When the slashed texture area at the middle of the block transformationmatrix has a large converting value, this implies that most areas of thecolor source image have slashed stripes. For instance, the color sourceimage is an image of rainfall.

When discrete cosine transformation is performed on an ordinary image,the high-frequency area at the bottom-right corner of the blocktransformation matrix is very small and can thus be neglected.

FIG. 5A is a schematic diagram illustrating a plurality of blocktransformation matrixes corresponding to respective gray-level blocksare generated after discrete cosine transformation is performed onrespective gray-level blocks. Each block transformation matrix hasfeatures as illustrated in FIG. 4.

FIG. 5B is a schematic diagram illustrating accumulation of each blocktransformation matrix of FIG. 5A. The converting values at correspondingpositions of each of the 5×5 block transformation matrixes as shown inFIG. 5A are accumulated to obtain an image transformation matrix asshown in FIG. 5C.

FIG. 5C is a schematic diagram illustrating an image transformationmatrix obtained by accumulating the block transformation matrixesaccording to an embodiment of the present invention. Each value of theimage transformation matrix is obtained by accumulating the blocktransformation matrixes as shown in FIG. 5B. Likewise, each blocktransformation matrix can be divided into a smooth texture area, avertical texture area, a horizontal texture area and a slashed texturearea.

Here, the texture features of an entire color source image are extractedand stored in the form of quantized values.

Thus, the present invention further defines a plurality of textureparameters. The texture parameters include a smooth texture parameterE_(DC) corresponding to the smooth texture area, a vertical textureparameter E_(V) corresponding to the vertical texture area, a horizontaltexture parameter E_(H) corresponding to the horizontal texture area,and a slashed texture parameter E_(S) corresponding to the slashedtexture area. The texture parameters are expressed in the followingequations.

Smooth texture parameter is expressed in Equation 2.

$\begin{matrix}{E_{DC} = {\sum\limits_{r = 1}^{\frac{M}{8} \times \frac{N}{8}}E_{{DC}_{r}}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$Slashed texture parameter is expressed in Equation 3.

$\begin{matrix}{E_{S} = {\sum\limits_{r = 1}^{\frac{M}{8} \times \frac{N}{8}}E_{S_{r}}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$Vertical texture parameter is expressed as in Equation 4.

$\begin{matrix}{E_{V} = {\sum\limits_{r = 1}^{\frac{M}{8} \times \frac{N}{8}}E_{V_{r}}}} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$Horizontal texture parameter is expressed as in Equation 5.

$\begin{matrix}{E_{H} = {\sum\limits_{r = 1}^{\frac{M}{8} \times \frac{N}{8}}E_{H_{r}}}} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

FIGS. 6A and 6B is a determination flowchart based on texture featuresaccording to an embodiment of the present invention. FIGS. 6A and 6Bcombine the approaches used in FIGS. 2A, 2B, 3A, 3B, 4, 5A, 5B and 5C.

Firstly, gray-level transformation is performed on a color source imageto generate a gray-level source image (step S21, FIGS. 2A and 2B).

Next, discrete cosine transformation is performed on a gray-level sourceimage to generate at least one block transformation matrix (step S23).Step S23 further includes sub-steps of S231˜S233. The gray-level sourceimage is divided into at least one gray-level block (step S231, FIG.3A). Discrete cosine transformation is respectively performed on the atleast one gray-level block for generating a block transformation matrixcorresponding to the gray-level block (step S233, FIG. 3B, 4, 5A).

As disclosed above, each block transformation matrix includes aplurality of converting values. For instance, during 8×8 DCT, each blocktransformation matrix includes 8×8 converting values. Then, a pluralityof texture parameters are obtained according to the block transformationmatrixes (step S25, FIGS. 5B and 5C).

Step S25 further includes sub-steps S251˜S255. The block transformationmatrixes are accumulated to obtain an image transformation matrix,wherein the image transformation matrix includes a plurality ofaccumulated converting values (step S251, FIG. 5B). The imagetransformation matrix is divided into a smooth texture area, a verticaltexture area, a horizontal texture area, a slashed texture area, and ahigh-frequency area (step S253). In addition, a smooth texture parameterE_(DC) is obtained according to the smooth texture area, a verticaltexture parameter E_(V) is obtained according to the vertical texturearea, a horizontal texture parameter E_(H) is obtained according to thehorizontal texture area, and a slashed texture parameter E_(S) isobtained according to the slashed texture area (step S255).

It can be known with reference to the descriptions of FIG. 5B that whenthe number of block transformation matrixes is plural, step S25 furtherincludes some sub-steps. The converting value at the first position ofthe first block transformation matrix and the converting value at thefirst position of the second block transformation matrix are summed up.The above summation process is repeated on the converting values at thefirst position of each block transformation matrix. The above processeson the converting values at positions of each block transformationmatrix are repeated to obtain an image transformation matrix.

The determination of color features of an image is disclosed below.

FIG. 7A is a schematic diagram illustrating a color source imagerepresented by a prime color representation method.

Firstly, let the chromaticity transformation matrix be exemplified asbelow.

$\quad\begin{bmatrix}0.607 & 0.174 & 0.2 \\0.299 & 0.587 & 0.114 \\0 & 0.066 & 1.111\end{bmatrix}$

Through the chromaticity transformation matrix, the first prime colorvalue (R), the second prime color value (G) and the third prime colorvalue (B) corresponding to the color pixel contained in a color sourceimage can be represented by a chromaticity representation method used inthe CIE XYZ color system.

The method for converting prime color values into chromaticity valuesthrough a chromaticity transformation matrix is expressed in Equation 6.

$\begin{matrix}{\begin{bmatrix}X \\Y \\Z\end{bmatrix} = {\begin{bmatrix}0.607 & 0.174 & 0.2 \\0.299 & 0.587 & 0.114 \\0 & 0.066 & 1.111\end{bmatrix}\begin{bmatrix}R \\G \\B\end{bmatrix}}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

That is, the pixels change to be represented by the first chromaticityvalue (X), the second chromaticity value (Y) and the third chromaticityvalue (Z). Here, the pixels represented by a chromaticity representationmethod are referred as chromaticity pixels.

FIG. 7B is a schematic diagram illustrating a color source imagerepresented by a chromaticity representation method. Here, theconversion relationship is one-to-one. The color pixel at the positionp(1,1) which was originally represented by RGB_p(1,1) is converted to achromaticity pixel XYZ_p(1,1).

For convenience of comparison, a chromaticity pixel may further benormalized as chromatic values ranging between 0 and 1.

FIG. 8 is a schematic diagram illustrating chromaticity values convertedto normalized chromatic values for each chromaticity pixel of the colorsource image.

As indicated in FIG. 8, the first chromaticity value (X), the secondchromaticity value (Y) and the third chromaticity value (Z) of eachchromaticity pixel are normalized altogether to generate a firstnormalized chromatic value and a second normalized chromatic value.

Normalized chromatic values are obtained from chromatic values accordingto Equation 7.

$\begin{matrix}{\begin{bmatrix}X^{\prime} \\Y^{\prime}\end{bmatrix} = \begin{bmatrix}\frac{X}{X + Y + Z} \\\frac{Y}{X + Y + Z}\end{bmatrix}} & \left( {{Equation}\mspace{14mu} 7} \right)\end{matrix}$

The first normalized chromatic value (X′) is expressed as the firstchromaticity value (X) divided by summation of the first chromaticityvalue (X), the second chromaticity value (Y) and the third chromaticityvalue (Z) of the chromaticity pixel, that is, X′=X/(X+Y+Z).

The second normalized chromatic value (Y′) is expressed as the secondchromaticity value (Y) divided by summation of the first chromaticityvalue (X), the second chromaticity value (Y) and the third chromaticityvalue (Z) of the chromaticity pixel, that is, Y′=Y/(X+Y+Z).

FIG. 9 is a schematic diagram illustrating a color source image mappedonto a chromaticity plane according to a first normalized chromaticvalue and a second normalized chromatic value. The horizontal axis ofthe chromaticity plane corresponds to the first normalized chromaticvalue X′. The vertical axis of the chromaticity plane corresponds to thesecond normalized chromatic value Y′.

FIG. 9 is equivalent to mapping the color information corresponding toeach color pixel of a color source image onto a two dimensionalchromaticity plane. Thus, number of points in the chromaticity plane asshown in FIG. 9 is equivalent to number of color pixels in the colorsource image.

For instance, X′Y′_p(1,2) represents the first and the second normalizedchromatic values corresponding to the color pixel at the first row andthe second column of the color source image. X′Y′_p(1,3) represents thefirst and the second normalized chromatic values corresponding to thecolor pixel at the first row and the third column of the color sourceimage. X′Y′_p(1,4) represents the first and the second normalizedchromatic values corresponding to the color pixel at the first row andthe fourth column of the color source image. The color pixels at otherpositions of the color source image can be obtained by analogy.

On the chromaticity plane, the same set of normalized chromatic valuesmay correspond to a plurality of pixels at the same time. This isbecause pixels at different positions in the color source image maycorrespond to the same chromaticity. Thus, a many-to-one correspondencebetween positions on the chromaticity plane and their correspondingpixels may exist.

For convenience of calculation, the normalized chromatic values can bequantized within a specific numeric interval. For instance, each of thefirst and the second normalized chromatic values is multiplied by 256and then is divided by 1000, such that the first and the secondnormalized chromatic values are quantized between 0˜256. The specificnumeric interval can be adjusted according to the needs in practicalapplication or the processing speed of the system.

Moreover, the present invention collects statistics relating to numberof pixels corresponding to each point on the chromaticity plane. Here, C(x′,y′) represents the number of pixels corresponding to the position(x′,y′) on the chromaticity plane. Based on the concept of the presentinvention, different chromaticity planes are obtained from differentcolor source images. Therefore, the number of pixels corresponding topositions on the chromaticity plane (statistical results) C (x′,y′) canbe used to represent the color features of a color source image.

The present invention also defines a combination of chromaticitydensities which is taken in conjunction with the number of pixelscorresponding to positions on the chromaticity plane.

The present invention firstly defines a sum of powers as the summationof power of the first normalized chromatic value and power of the secondnormalized chromatic value. For simplification purpose, let thesummation of powers be expressed as p+q, wherein the first power pcorresponds to the first normalized chromatic value and the second powerq corresponds to the second normalized chromatic value.

Given that the summation of powers p+q≦2, the combination ofchromaticity densities composed of the first power p and the secondpower q may have 6 scenarios being (p=0,q=0), (p=0,q=1), (p=1,q=0),(p=0, q=2), (p=1,q=1), and (p=2,q=0).

Then, the calculation of color momentum m_(pq) according to thecombination of chromaticity densities and the number of pixelscorresponding to positions on the chromaticity plane is expressed inEquation 8.

$\begin{matrix}{m_{pq} = {\sum\limits_{x = 0}^{X_{L}}{\sum\limits_{y = 0}^{Y_{L}}{x^{p}y^{q}{C\left( {x,y} \right)}}}}} & \left( {{Equation}\mspace{14mu} 8} \right)\end{matrix}$

Wherein, X_(L), Y_(L) represent a quantized value 256. It can be knownfrom the above equation that the number of color momentums is determinedaccording to the number of combinations of chromaticity densities. Thus,given that the sum of powers satisfies p+q≦2, 6 color momentums arecorrespondingly generated.

$\begin{matrix}{\mspace{79mu}{{{First}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{00}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{0}y^{0}{C\left( {x,y} \right)}}}}}} & \left( {{Equation}\mspace{14mu} 9} \right) \\{{{Second}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{01}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{0}y^{1}{C\left( {x,y} \right)}}}}} & \left( {{Equation}\mspace{14mu} 10} \right) \\{\mspace{79mu}{{{Third}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{10}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{1}y^{0}{C\left( {x,y} \right)}}}}}} & \left( {{Equation}\mspace{14mu} 11} \right) \\{{{Fourth}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{11}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{1}y^{1}{C\left( {x,y} \right)}}}}} & \left( {{Equation}\mspace{14mu} 12} \right) \\{\mspace{79mu}{{{Fifth}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{02}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{0}y^{2}{C\left( {x,y} \right)}}}}}} & \left( {{Equation}\mspace{14mu} 13} \right) \\{\mspace{79mu}{{{Sixth}\mspace{14mu}{color}\mspace{14mu}{momentum}\mspace{14mu} m_{20}} = {\sum\limits_{x = 0}^{X_{256}}{\sum\limits_{y = 0}^{Y_{256}}{x^{2}y^{0}{C\left( {x,y} \right)}}}}}} & \left( {{Equation}\mspace{14mu} 14} \right)\end{matrix}$

Although it is assumed that the sum of powers is less than or equal to 2(p+q≦2) in above equations, the summation of powers may be equivalent toother values (such as less than or equal to 3) in practical application.Consequentially, the combination of chromaticity densities composed ofthe first power p and the second power q may change, and the number ofcolor momentums will change accordingly.

FIGS. 10A and 10B respectively are a flowchart of obtaining colormomentums based on color features according to an embodiment of thepresent invention. It can be known from FIGS. 7A, 7B, 8 and 9, the stepS12 of proceeding texture feature extraction on the color source imageto obtain a plurality of color momentums includes steps S31-S39.

In step S31, chromaticity conversion is performed on color pixelsrespectively to obtain a plurality of chromaticity pixels (FIGS. 7A,7B). In step S33, the plurality of chromaticity pixels are mapped ontothe chromaticity plane (FIG. 9). In step S35, number of chromaticitypixels corresponding to positions on the chromaticity plane iscalculated/counted. In step S37, a plurality of combinations ofchromaticity densities are defined. In step S39, a plurality of colormomentums are obtained according to the plurality of combinations ofchromaticity densities and the number of pixels corresponding topositions on the chromaticity plane.

Step S31 further includes steps S311 and S313. In step S311, achromaticity transformation matrix is provided. In step S313, colorpixels represented by a prime color representation method are convertedinto chromaticity pixels represented by a chromaticity representationmethod according to the chromaticity transformation matrix (step S313).

Step S33 further includes steps S331 and S333. In step S331, a firstnormalized chromatic value X′ and a second normalized chromatic value Y′representing a chromaticity pixel are obtained according to the first,the second and the third chromaticity values of the chromaticity pixel.In step S333, a position of the chromaticity pixel on a chromaticityplane is determined according to the first normalized chromatic value X′and the second normalized chromatic value Y′ representing thechromaticity pixel.

Step S331 is for obtaining the first and the second normalized chromaticvalues of each chromaticity pixel, and includes following sub-steps.

The first normalized chromatic value representing the first chromaticitypixel (X_(p11)/(X_(p11)+Y_(p11)+Z_(p11))) is obtained according to thefirst chromaticity value (X_(p11)) of the first chromaticity pixel and asummation of the first chromaticity value (X_(p11)), the secondchromaticity value (Y_(p11)) and the third chromaticity value (Z_(p11))of the first chromaticity pixel (the summation=X_(p11)+Y_(p11)+Z_(p11)).

The second normalized chromatic value representing the firstchromaticity pixel (Y_(p11)/(X_(p11)+Y_(p11)+Z_(p11))) is obtainedaccording to the second chromaticity value (Y_(p11)) of the firstchromaticity pixel and a summation of the first chromaticity value(X_(p11)) of the first chromaticity pixel plus the second chromaticityvalue (Y_(p11)) and the third chromaticity value (Z_(p11)) of the firstchromaticity pixel (the summation=X_(p11)+Y_(p11)+Z_(p11)). The aboveprocess is repeated and applied to each of the chromaticity pixels(P₁₁˜P_(MN)).

Step S331 further includes sub-steps of: quantizing on the firstnormalized chromatic value (X_(p11)/(X_(p11)+Y_(p11)+Z_(p11))) and thesecond normalized chromatic value (Y_(p11)/(X_(p11)+Y_(p11)+Z_(p11)))representing the chromaticity pixel.

Step S39 further includes steps S391 and S393.

In step S391, power of the first normalized chromatic value and power ofthe second normalized chromatic value corresponding to positions on thechromaticity plane are adjusted in response to each combination of thechromaticity densities. In step S393, a product of power of the firstnormalized chromatic, power of the second normalized chromatic valuecorresponding to positions on the chromaticity plane and number ofpixels corresponding to positions on the chromaticity plane iscalculated to obtain the plurality of color momentums.

FIG. 11 is a schematic diagram illustrating image identificationparameters generated according to texture parameters and colormomentums. FIG. 6 and FIG. 10 illustrate how the texture parameters andthe color momentums are obtained. FIG. 11 illustrates how the imageidentification parameter F_(pic) is generated according to the textureparameters and the color momentums.

Please refer to FIG. 11. In step S13, the texture identificationparameter and the color identification parameter are obtainedrespectively and then the texture identification parameter F_(texture)and the color identification parameter F_(color) are summed up to obtainan image identification parameter F_(pic).

That is, the texture parameters and the size of the color source imageare weighted to obtain the texture identification parameter F_(texture).The texture parameters include a smooth texture parameter E_(DC), aslashed texture parameter E_(S), a horizontal texture parameter E_(H),and a vertical texture parameter E_(V).

The color momentums and the size of the color source image are weightedto obtain a color identification parameter F_(color). The color momentumis calculated according to combinations of chromaticity densities andnumber of chromaticity pixels corresponding to positions on thechromaticity plane.

Then, the texture identification parameter F_(texture) and the coloridentification parameter F_(color) are weighted and summed up to obtainan image identification parameter F_(pic).

The descriptions for generating the texture identification parameterF_(texture) and the color identification parameter F_(color)respectively are disclosed below.

Firstly, the generation of the texture identification parameterF_(texture) is disclosed below.

Corresponding weights of texture parameters can be adjusted according tothe size of the color source image before weighting calculation isperformed. For instance, when the color source image is larger than 1000times, each texture parameter is multiplied by 2. This step can beselectively added.

The generation of the texture identification parameter F_(texture) isdefined as: weighting the plurality of texture parameters the textureparameter according to a smooth texture weight a corresponding to thesmooth texture parameter, a vertical texture weight b corresponding tothe vertical texture parameter, a horizontal texture weight ccorresponding to the horizontal texture parameter, and a slashed textureweight d corresponding to the slashed texture parameter.

It should be noted that apart from setting the texture identificationparameter F_(texture) as the summation of products of the textureparameters and their respective texture weights, the textureidentification parameter F_(texture) can be calculated according toproportions of texture parameters. A number of possible calculations areexemplified below.

$\begin{matrix}{F_{texture} = {{a \times {E_{DC}}} + {b \times {E_{V}}} + {c \times {E_{H}}} + {d \times {E_{S}}}}} & \left( {{Equation}\mspace{14mu} 15} \right) \\{F_{texture} = {{a \times {E_{DC}}} + {\frac{b}{c} \times {\frac{E_{V}}{E_{H}}}} + {d \times {E_{S}}}}} & \left( {{Equation}\mspace{14mu} 16} \right) \\{F_{texture} = {{a \times {E_{DC}}} + {\frac{c}{b} \times {\frac{E_{H}}{E_{V}}}} + {d \times {E_{s}}}}} & \left( {{Equation}\mspace{14mu} 17} \right) \\{F_{texture} = {{\frac{a}{d} \times {\frac{E_{DC}}{E_{S}}}} + {\frac{c}{b} \times {\frac{E_{H}}{E_{V}}}}}} & \left( {{Equation}\mspace{14mu} 18} \right)\end{matrix}$

Let Equation 16 be taken for example. Given that a=0.7, b/c=0.15,d=0.15, the value of the texture identification parameter F_(texture) isexpressed in Equation 19.

$\begin{matrix}{F_{texture} = {{0.7 \times {E_{DC}}} + {0.15 \times {\frac{E_{V}}{E_{H}}}} + {0.15 \times {E_{S}}}}} & \left( {{Equation}\mspace{14mu} 19} \right)\end{matrix}$

Under ordinary circumstances, the color source image contains a highproportion of smooth texture. Thus, it is assumed that the weight ofsmooth texture is the maximum among the texture weights.

When calculating the texture identification parameter F_(texture), thevalues of texture parameters and corresponding texture weights (a, b, c,d) of the texture parameters are only used for representing the weightvalues corresponding to the texture parameters, and the values ofweights a, b, c, d may vary with the attributes of the color sourceimage. For instance, when the image has a higher proportion ofhorizontal texture and the contrast of horizontal texture is emphasized.In such case, the weight value corresponding to the horizontal texturefeature becomes larger.

Moreover, the values of weights a, b, c, d may vary in differentequations.

Besides, the values of texture weights may change according to the typeof the image as long as the summation of the coefficients at theright-hand side of each equation disclosed above is equal to 1.

The generation of the color identification parameter F_(color) isdisclosed below.

The color identification parameter F_(color) corresponding to the colorsource image is calculated according to a plurality of color momentums(m₀₀, m₀₁, m₁₀, m₁₁, m₀₂, m₂₀). To simplify the calculation process, thecolor momentums having minor influence can be neglected in thecalculation of color identification parameters. For instance, the firstcolor momentum |m₀₀| can be neglected.

In addition, the corresponding weights of the color momentums can beadjusted according to the size of the color source image. Thus, a sizescaling factor z is used as a weight for each chromaticity momentum.F _(color) =z×|m ₀₀ |+z×|m ₀₁ |+z×|m ₁₀ |+z×|m ₁₁ |+z×|m ₀₂ |+z×|m₂₀|  (Equation 20)

The scaling weight z is provided because the color source image may havedifferent aspect ratios.

The color source image with larger size has more pixels, and accordinglyhas more points on the corresponding chromaticity plane. Thus, there aremore chromaticity pixels corresponding to the same position on thechromaticity plane. When calculating the color identificationparameters, the size scaling factor z is added to resolve the differencein image size.

Let the size of the first color source image A1 be 100×100. When thecolor source image is 100×100, the size scaling factor is equal to 1.Then, the calculation of the color identification parameter of the firstcolor source image A1 is expressed in Equation 21.F _(color-A) =|m ₀₀ ^(A) |+|m ₀₁ ^(A) |+|m ₁₀ ^(A) |+|m ₁₁ ^(A) |+|m ₀₂^(A) |+|m ₂₀ ^(A)|  (Equation 21)

On the other hand, when the second color source image A2 and the firstcolor source image A1 have the same contents. However, the second colorsource image is with the size of are 400×400. In other words, the sizeof the second color source image A2 is (400*400)/(100*100)=16 timeslarger than the first color source image A1. By setting the size scalingfactor to be 1/16, F_(color-B) will then be equal to F_(color-A).

$\begin{matrix}{F_{{color} - B} = {{\frac{1}{16} \times {m_{00}^{B}}} + {\frac{1}{16} \times {m_{01}^{B}}} + {\frac{1}{16} \times {m_{10}^{B}}} + {\frac{1}{16} \times {m_{11}^{B}}} + {\frac{1}{16} \times {m_{02}^{B}}} + {\frac{1}{16} \times {m_{20}^{B}}}}} & \left( {{Equation}\mspace{14mu} 22} \right)\end{matrix}$

When the above process is repeated on different color source images, thetexture parameters (E_(DC), E_(V), E_(H), E_(S)) and the color momentums(m00, m01, m10, m11, m02, m20) corresponding to the source images can beobtained. Then, the texture identification parameter F_(texture) and thecolor identification parameter F_(color) corresponding to the colorsource image are obtained.

For instance, the first texture identification parameter F_(texture-A)and the first color identification parameter F_(color-A) correspond tothe first color source image A, and the second texture identificationparameter F_(texture-B) and the second color identification parameterF_(color-B) correspond to the second color source image B.

Degree of similarity between the first color source image A and thesecond color source image B can be determined according to the textureidentification parameter F_(texture) and the color identificationparameter F_(color) corresponding to the two color source images.

The image differential parameter D_(AB) is defined as a summation ofdifference between two texture parameters and the difference between twocolor momentums. The image differential parameter D_(AB) is expressed inEquation 23.D _(AB) =|F _(texture-A) −F _(texture-B) |+|F _(color-A) −F_(color-B)|  (Equation 23)

When the image differential parameter is calculated according toEquation 23, it must be confirmed that the first color source image Aand the second color source image B are consistent in terms of the basisfor calculating the texture parameters and the color momentums.

For instance, the numbers and values of texture weights must be the samewhen calculating texture parameters. On the other hand, the size scalingfactor must be taken into consideration in the calculation of colormomentums.

The difference between each color source image of the database and theto-be-tested color source image is calculated according to Equation 23.Following the computation of Equation 23, the color source image withminimum difference can be easily located from the database.

FIG. 12A is a schematic diagram illustrating a first color source imagepresumably used for comparison. As indicated in FIG. 12A, the firstcolor source image is presumably divided into two halves: a left halfand a right half. The left-half block has meshed shedding in blue color,and the right-half block has dotted shedding in red color. Presumably,there are vertical stripes in black/white colors near the lower edge ofthe first source image.

FIG. 12B is a schematic diagram illustrating determination of degree ofsimilarity between a first color source image and a second color sourceimage according to a comparison between the two color source images.FIG. 12B is similar to FIG. 12A except that the left-half and theright-half blocks of the second source image of FIG. 12B are opposite tothat of the first source image of FIG. 12B.

Given that the determination of image similarity is based on colorfeatures alone, two different color source images, such as FIGS. 12A and12B, may be mistaken as the same image if proportion of the red colorand the blue color are the same. When the identification method of thepresent invention is used, texture features are taken into considerationand it is determined that the texture features of the two color sourceimages are not the same.

FIG. 13 is a block diagram illustrating an electronic device with imageidentification function according to an embodiment of the presentinvention. The electronic device 5 as shown in FIG. 13 may refer to anyelectronic products storing color source images such as server of searchengine, personal computer, and digital photo frame etc.

The electronic device 5 of the present invention includes a storage unit51, a texture determination unit 53, a color determination unit 55 andan identification unit 57. Both the texture determination unit 53 andthe color determination unit 55 are electrically connected to thestorage unit 51 and the identification unit 57.

The storage unit 51 stores a color source image. The texturedetermination unit 53 extracts texture features from the color sourceimage to obtain the texture parameters. The color determination unit 55extracts color features from the color source image to obtain the colormomentums.

The identification unit 57 obtains an image identification parameterF_(pic) after weighting the plurality of texture parameters and theplurality of color momentums. Also, the identification unit 57 canselectively adjust the weights corresponding to the texture parametersand the color momentums according to the size of the color source image.

With respect to the application in the search and comparison of images,the electronic device 5 performs similar processing on a plurality ofcolor source images. The storage unit 51 respectively provides differentmemory addresses in which different color source images (such as thefirst color source image and the second color source image) are stored.

Accordingly, the texture determination unit 53 and the colordetermination unit 55 extract texture features parameters and colormomentums from the color source images. Then, the identification unit 57generates a first image identification parameter corresponding to thefirst color source image and a second image identification parametercorresponding to the second color source image.

The identification unit 57 may include a comparison module (notillustrated) which compares the first image identification parameterwith the second image identification parameter. The closer to each otherthe first image identification parameter and the second imageidentification parameter are, the higher the degree of similaritybetween the first color source image and the second color source imagewill be.

The texture determination unit 53 includes a gray-level transformationmodule 531, a discrete cosine conversion module 533, a texture featurecapturing module 535, a block division module 537, and an accumulationmodule 539.

The gray-level transformation module 531 is electrically connected tothe storage unit 51. The gray-level transformation module 531 transformsthe color source image to generate a gray-level source image.

The block division module 537 is electrically connected to thegray-level transformation module 531 for dividing the gray-level sourceimage into a plurality of gray-level blocks.

The discrete cosine conversion module 533 is electrically connected tothe gray-level transformation module 531 and the block division module537 for performing discrete cosine transformation on the gray-levelsource image to generate a plurality of block transformation matrixes.

The texture feature capturing module 535 is electrically connected tothe discrete cosine conversion module 533 for obtaining textureparameters according to the block transformation matrixes.

The accumulation module 539 is electrically connected to the texturefeature capturing module 535 and the identification unit 57 foraccumulating the block transformation matrixes to obtain an imagetransformation matrix.

The color determination unit 55 includes a chromaticity conversionmodule 551, a mapping module 553, a pixel accumulated module 555, and acolor feature capturing module 557.

The chromaticity conversion module 551 is electrically connected to thestorage unit 51. The chromaticity conversion module 551 converts theplurality of color pixels into the plurality of chromaticity pixelsaccording to the chromaticity transformation matrix which can bepre-stored in the storage unit 51.

The mapping module 553 is electrically connected to the chromaticityconversion module 551. The mapping module 553 maps the plurality ofchromaticity pixels onto a chromaticity plane.

The pixel accumulated module 555 is electrically connected to themapping module 553. The pixel accumulated module 555 calculates thenumber of pixels corresponding to positions on the chromaticity plane.

The color feature capturing module 557 is electrically connected to thepixel accumulated module 555 and the identification unit 57. The colorfeature capturing module 557 obtains the color momentums according tothe combinations of chromaticity densities and the number of pixelscorresponding to positions on the chromaticity plane.

FIG. 14 is a schematic diagram illustrating image identification basedon three color source images. Suppose the first color source image isthe image used by the user for searching purpose. The second colorsource image and the third the color source image are existing images inthe database. As disclosed above, in response to the difference in imagesize, a scaling factor is taken into consideration when calculatingimage identification parameters.

It can be known from the above disclosure that the first imageidentification parameter for the first color source image can beobtained through calculation. Likewise, the second image identificationparameter for the second color source image can be obtained throughcalculation, and so can the third image identification parameter for thethird the color source image be obtained through calculation.

Then, a difference between the first image identification parameter andthe second image identification parameter and a difference between thefirst image identification parameter and the third image identificationparameter are calculated, and a comparison between the two differencesis made accordingly.

If the difference between the first and the second image identificationparameters is smaller than the difference between the first and thethird image identification parameters, this implies that the first colorsource image is more similar to the second color source image than tothe third color source image.

Conversely, if the difference between the first and the third imageidentification parameters is smaller than the difference between thefirst and the second image identification parameters, this implies thatthe first color source image is more similar to the third color sourceimage than to the second color source image.

By the same token, when the search engine or the database inside theelectronic device has many existing source images, the image mostsimilar to the to-be-retrieved color source image can be locatedaccording to the image identification process disclosed above.

Based on the concept of the present invention, when the search engine orthe electronic device provides image search function, a pre-determinedreference image size can be provided. The reference image size can beapplied on each color source image and used as a comparison reference,and a corresponding scaling factor is determined accordingly. Once thescaling factor is determined, the weights of the texture identificationparameter Ftexture and the color identification parameter Fcolor areadjusted according to the scaling factor to calculate a correspondingimage identification parameter F_(pic).

Then, the color source images and their corresponding imageidentification parameters are stored. When image search is performed,the reference image size is applied on the to-be-retrieved color sourceimage. By adjusting the weight according to the scaling factor, theimage identification parameter corresponding to the to-be-retrievedcolor source image can be obtained through calculation.

The images pre-stored in the database are searched such that the imagewhose image identification parameter is closest to the imageidentification parameter of the to-be-retrieved color source image canbe located from the database. Among images of the database, the locatedimage is the image most similar to the to-be-retrieved color sourceimage.

The invention can further be used in any computer program productstoring a software program. When the software program is executed, theelectronic device with a controller will perform the imageidentification method disclosed above. Or, the computer program productmay perform image identification with texture feature or color featurealone.

When the identification method of the present invention obtains an imageidentification parameter, factors such as colors (chromaticity), colordistribution, shapes (texture) and perceiving depth and size (scale) aretaken into consideration. The identification method of the presentinvention is close to users' intuition and similar to the naked eyecomparison. Therefore, the image identification function provided in thepresent invention is more conformed to users' needs. The inventionprovides CBIR function such that the input for image search is notlimited to texts, and the process of image search is made moreconvenient.

Anyone who is skilled in the technology field of the present inventionwill understand that various logic blocks, modules, circuits andprocedures used for exemplification purpose in the above descriptionscan be implemented by electronic hardware, computer software, or acombination thereof. The connection mode can be implemented by way of incommunication with, connection, coupling, electrical connection oralternative approaches. These connection modes illustrate that in theimplementation of logic blocks, modules, circuits and procedures,signals can be exchanged in a direct or indirect manner to exchange ortransmit signals, data and control information through different means.For instance, signals, data and control information can be exchanged ortransmitted through cabled electronic signals, wireless electromagneticsignals and optical signals. Thus, the terminologies used in the presentspecification are not restrictive in the implementation of connectionrelationship of the present invention, and the scope of protection ofthe present invention will not be affected by the connection mode.

While the invention has been described by way of example and in terms ofthe preferred embodiment(s), it is to be understood that the inventionis not limited thereto. On the contrary, it is intended to cover variousmodifications and similar arrangements and procedures and the scope ofthe appended claims therefore should be accorded the broadestinterpretation so as to encompass all such modifications and similararrangements and procedures.

What is claimed is:
 1. An image identification method applied to a colorsource image and executed by an electronic device, comprising steps of:storing the color source image; proceeding texture feature extraction onthe color source image to obtain a plurality of texture parameters;proceeding color feature extraction on the color source image to obtaina plurality of color momentums; weighting the plurality of textureparameters and the plurality of color momentums to obtain an imageidentification parameter corresponding to the color source image; andadjusting weights corresponding to the plurality of texture parametersand weights corresponding to the plurality of color momentums accordingto size of the color source image.
 2. The identification methodaccording to claim 1, wherein the step of proceeding texture featureextraction on the color source image to obtain the plurality of textureparameters comprises steps of: converting the color source image to agray-level source image; performing discrete cosine transformation onthe gray-level source image to generate at least one blocktransformation matrix; and, obtaining the plurality of textureparameters according to the at least one block transformation matrix. 3.The identification method according to claim 2, wherein the gray-levelsource image comprises a plurality of gray-level pixels whose graylevels are obtained by calculating a plurality of color pixels containedin the color source image according to a gray-level transformationequation.
 4. The identification method according to claim 3, wherein thegray-level transformation equation defines a converting weight of firstprime color corresponding to a first prime color value, a convertingweight of second prime color corresponding to a second prime colorvalue, and a converting weight of third prime color corresponding to athird prime color value.
 5. The identification method according to claim4, wherein the gray levels corresponding to the plurality of gray-levelpixels are obtained by summing up a product of the first prime colorvalue and the converting weight of first prime color, a product of thesecond prime color value and the converting weight of second primecolor, and a product of the third prime color value and the convertingweight of third prime color.
 6. The identification method according toclaim 2, wherein the step of performing discrete cosine transformationon the gray-level source image to generate the at least one blocktransformation matrix further comprises steps of: dividing thegray-level source image into at least one gray-level block; and,performing discrete cosine transformation on each of the at least onegray-level block for respectively generating the at least one blocktransformation matrix.
 7. The identification method according to claim6, wherein the color source image comprises M×N pixels, and number ofthe at least one gray-level block and number of pixels contained in eachof the at least one gray-level block are determined according to formatof the discrete cosine transformation.
 8. The identification methodaccording to claim 7, wherein each of the at least one gray-level blockrespectively comprises 8×8 gray-level pixels if the format of thediscrete cosine transformation is 8×8.
 9. The identification methodaccording to claim 2, wherein the step of obtaining the plurality oftexture parameters according to the at least one block transformationmatrix further comprises steps of: accumulating the at least one blocktransformation matrix to obtain an image transformation matrix, whereinthe image transformation matrix comprises a plurality of accumulatedconverting values; dividing the image transformation matrix into asmooth texture area, a vertical texture area, a horizontal texture area,a slashed texture area, and a high-frequency area; and, obtaining theplurality of texture parameters according to the smooth texture area,the vertical texture area, the horizontal texture area, and the slashedtexture area.
 10. The identification method according to claim 9,wherein the plurality of texture parameters comprise a smooth textureparameter corresponding to the smooth texture area, a vertical textureparameter corresponding to the vertical texture area, a horizontaltexture parameter corresponding to the horizontal texture area, and aslashed texture parameter corresponding to the slashed texture area. 11.The identification method according to claim 10, wherein the step ofweighting the plurality of texture parameters and the plurality of colormomentums to obtain the image identification parameter corresponding tothe color source image further comprises steps of: weighting theplurality of texture parameters according to a smooth texture weightcorresponding to the smooth texture parameter, a vertical texture weightcorresponding to the vertical texture parameter, a horizontal textureweight corresponding to the horizontal texture parameter, and a slashedtexture weight corresponding to the slashed texture parameter.
 12. Theidentification method according to claim 11, wherein the smooth textureweight is maximum of the texture weights.
 13. The identification methodaccording to claim 9, wherein the at least one block transformationmatrix comprises a plurality of converting values, and when the numberof the at least one block transformation matrix is plural, the step ofaccumulating the at least one block transformation matrix to obtain theimage transformation matrix further comprises steps of: summing up aconverting value at a first position of a first block transformationmatrix and a converting value at the first position of a second blocktransformation matrix; repeating the above process on converting valuesat the first position of each block transformation matrix; and,obtaining the image transformation matrix after repeating the aboveprocess on converting values at each position of each blocktransformation matrix.
 14. The identification method according to claim1, wherein the color source image comprises a plurality of color pixelsand the step of proceeding color feature extraction on the color sourceimage to obtain the plurality of color momentums comprises steps of:performing chromaticity conversion on the plurality of color pixelsrespectively to obtain a plurality of chromaticity pixels; mapping theplurality of chromaticity pixels onto a chromaticity plane; calculatingnumber of the plurality of chromaticity pixels corresponding topositions on the chromaticity plane; defining a plurality ofcombinations of chromaticity densities; and, obtaining the plurality ofcolor momentums according to the plurality of combinations ofchromaticity densities and the number of the plurality of chromaticitypixels corresponding to the positions on the chromaticity plane.
 15. Theidentification method according to claim 14, wherein the step ofperforming chromaticity conversion on the plurality of color pixelsrespectively to obtain the plurality of chromaticity pixels comprisessteps of: providing a chromaticity transformation matrix; and,converting the plurality of color pixels represented by a prime colorrepresentation method into the plurality of chromaticity pixelsrepresented by a chromaticity representation method according to thechromaticity transformation matrix.
 16. The identification methodaccording to claim 15, wherein the prime color representation methodrepresents the plurality of color pixels with a first prime color value,a second prime color value and a third prime color value, and thechromaticity representation method represents the plurality ofchromaticity pixels with a first chromaticity value, a secondchromaticity value and a third chromaticity value.
 17. Theidentification method according to claim 16, wherein the step of mappingthe plurality of chromaticity pixels onto the chromaticity planecomprises steps of: representing each of the plurality of chromaticitypixel with a first normalized chromatic value and a second normalizedchromatic value, wherein the first and the second normalized chromaticvalues are obtained according to the first, the second and the thirdchromaticity values of each of the plurality of chromaticity pixels;and, determining the positions of the plurality of chromaticity pixelson the chromaticity plane according to the first and the secondnormalized chromatic values representing each of the plurality ofchromaticity pixels.
 18. The identification method according to claim17, wherein the step of representing each of the plurality ofchromaticity pixels with the first normalized chromatic value and thesecond normalized chromatic value comprises steps of: obtaining thefirst normalized chromatic value representing a first chromaticity pixelaccording to the first chromaticity value of a first chromaticity pixeland summation of the first, the second and the third chromaticity valuesof the first chromaticity pixel; obtaining the second normalizedchromatic value representing the first chromaticity pixel according tothe second chromaticity value of the first chromaticity pixel andsummation of the first, the second and the third chromaticity values ofthe first chromaticity pixel; and, repeating the above steps on each ofthe plurality of chromaticity pixels.
 19. The identification methodaccording to claim 18, wherein the step of representing each of theplurality of chromaticity pixels with the first normalized chromaticvalue and the second normalized chromatic value further comprises stepsof: quantizing the first and the second normalized chromatic valuesrepresenting each of the plurality of chromaticity pixels.
 20. Theidentification method according to claim 18, wherein the step ofobtaining the plurality of color momentums according to the plurality ofcombinations of chromaticity densities and the number of the pluralityof chromaticity pixels corresponding to the positions on thechromaticity plane comprises steps of: adjusting powers of the first andthe second normalized chromatic values corresponding to the positions onthe chromaticity plane in response to each of the plurality ofcombinations of chromaticity densities; and, calculating products ofpower of the first normalized chromatic value, power of the secondnormalized chromatic value, and number of chromaticity pixelscorresponding to the positions on the chromaticity plane to obtain theplurality of color momentums.
 21. The identification method according toclaim 1, wherein number of the plurality of color momentums isdetermined according to number of the plurality of combinations ofchromaticity densities.
 22. A non-transitory computer readable mediumstoring a software program, wherein when the software program isexecuted, an electronic device with a controller performs an imageidentification method comprising steps of: proceeding texture featureextraction on a color source image to obtain a plurality of textureparameters; proceeding color feature extraction on the color sourceimage to obtain a plurality of color momentums; weighting the pluralityof texture parameters and the plurality of color momentums to obtain animage identification parameter corresponding to the color source image;and adjusting weights corresponding to the plurality of textureparameters and weights corresponding to the plurality of color momentumsaccording to size of the color source image.